Model fit

Column

Assumption checks

Column

Indices of model fit

Random effect variances not available. Returned R2 does not account for random effects.
Metric Value
AIC 194.05
AICc -71.67
BIC 229.39
R2 (cond.)
R2 (marg.) 0.00
RMSE 3.02
Sigma 3.36

For interpretation of performance metrics, please refer to this documentation.

Parameter estimates

Column

Plot

Column

Tabular summary

# Fixed Effects
Parameter Coefficient SE t(-6)
(Intercept) 55.93 1.10 50.88
# Random Effects
Parameter Coefficient
SD (Intercept: N) 6.76
SD (block2: N) 5.17
SD (block3: N) 7.59
SD (block4: N) 3.79
SD (block5: N) 6.07
SD (block6: N) 4.50
SD (P1: N) 2.07
Cor (Intercept~block2: N) -0.88
Cor (Intercept~block3: N) -0.67
Cor (Intercept~block4: N) 0.07
Cor (Intercept~block5: N) -0.71
Cor (Intercept~block6: N) -0.95
Cor (Intercept~P1: N) -0.72
Cor (block2~block3: N) 0.94
Cor (block2~block4: N) -0.54
Cor (block2~block5: N) 0.28
Cor (block2~block6: N) 0.99
Cor (block2~P1: N) 0.29
Cor (block3~block4: N) -0.79
Cor (block3~block5: N) -0.05
Cor (block3~block6: N) 0.87
Cor (block3~P1: N) -0.05
Cor (block4~block5: N) 0.65
Cor (block4~block6: N) -0.39
Cor (block4~P1: N) 0.65
Cor (block5~block6: N) 0.44
Cor (block5~P1: N) 1.00
Cor (block6~P1: N) 0.45
SD (Residual) 3.36

To find out more about table summary options, please refer to this documentation.

Predicted Values

Column

Plot

Error in if (nrow * ncol < n) {: missing value where TRUE/FALSE needed

Column

Tabular summary

Text reports

Column

Textual summary

Random effect variances not available. Returned R2 does not account for random effects. Random effect variances not available. Returned R2 does not account for random effects. Random effect variances not available. Returned R2 does not account for random effects. We fitted a constant (intercept-only) linear mixed model (estimated using REML and nloptwrap optimizer) to predict yield (formula: yield ~ 1). The model included block as random effects (formula: ~block + P + 1 | N). . The model’s intercept is at 55.93 (t(-6) = 50.88). Within this model:

  • ()

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald t-distribution approximation.

Column

Model information

---
title: "Regression model summary from `{easystats}`"
output: 
  flexdashboard::flex_dashboard:
    theme:
      version: 4
      # bg: "#101010"
      # fg: "#FDF7F7" 
      primary: "#0054AD"
      base_font:
        google: Prompt
      code_font:
        google: JetBrains Mono
params:
  model: model
  check_model_args: check_model_args
  parameters_args: parameters_args
  performance_args: performance_args
---

```{r setup, include=FALSE}
library(flexdashboard)
library(easystats)

# Since not all regression model are supported across all packages, make the
# dashboard chunks more fault-tolerant. E.g. a model might be supported in
# `{parameters}`, but not in `{report}`.
#
# For this reason, `error = TRUE`
knitr::opts_chunk$set(
  error = TRUE,
  out.width = "100%"
)
```

```{r}
# Get user-specified model data
model <- params$model

# Is it supported by `{easystats}`? Skip evaluation of the following chunks if not.
is_supported <- insight::is_model_supported(model)

if (!is_supported) {
  unsupported_message <- sprintf(
    "Unfortunately, objects of class '%s' are not yet supported in {easystats}.\n
    For a list of supported models, see `insight::supported_models()`.",
    class(model)[1]
  )
}
```


Model fit 
=====================================  

Column {data-width=700}
-----------------------------------------------------------------------

### Assumption checks

```{r check-model, eval=is_supported, fig.height=10, fig.width=10}
check_model_args <- c(list(model), params$check_model_args)
do.call(performance::check_model, check_model_args)
```

```{r, eval=!is_supported}
cat(unsupported_message)
```

Column {data-width=300}
-----------------------------------------------------------------------

### Indices of model fit

```{r, eval=is_supported}
# `{performance}`
performance_args <- c(list(model), params$performance_args)
table_performance <- do.call(performance::performance, performance_args)
print_md(table_performance, layout = "vertical", caption = NULL)
```


```{r, eval=!is_supported}
cat(unsupported_message)
```

For interpretation of performance metrics, please refer to <a href="https://easystats.github.io/performance/reference/model_performance.html" target="_blank">this documentation</a>.

Parameter estimates
=====================================  

Column {data-width=550}
-----------------------------------------------------------------------

### Plot

```{r dot-whisker, eval=is_supported}
# `{parameters}`
parameters_args <- c(list(model), params$parameters_args)
table_parameters <- do.call(parameters::parameters, parameters_args)

plot(table_parameters)
```


```{r, eval=!is_supported}
cat(unsupported_message)
```

Column {data-width=450}
-----------------------------------------------------------------------

### Tabular summary

```{r, eval=is_supported}
print_md(table_parameters, caption = NULL)
```


```{r, eval=!is_supported}
cat(unsupported_message)
```

To find out more about table summary options, please refer to <a href="https://easystats.github.io/parameters/reference/model_parameters.html" target="_blank">this documentation</a>.


Predicted Values
=====================================  

Column {data-width=600}
-----------------------------------------------------------------------

### Plot

```{r expected-values, eval=is_supported, fig.height=10, fig.width=10}
# `{modelbased}`
int_terms <- find_interactions(model, component = "conditional", flatten = TRUE)
con_terms <- find_variables(model)$conditional

if (is.null(int_terms)) {
  model_terms <- con_terms
} else {
  model_terms <- clean_names(int_terms)
  int_terms <- unique(unlist(strsplit(clean_names(int_terms), ":", fixed = TRUE)))
  model_terms <- c(model_terms, setdiff(con_terms, int_terms))
}

text_modelbased <- lapply(unique(model_terms), function(i) {
  grid <- get_datagrid(model, at = i, range = "grid", preserve_range = FALSE)
  estimate_expectation(model, data = grid)
})

ggplot2::theme_set(theme_modern())
# all_plots <- lapply(text_modelbased, function(i) {
#   out <- do.call(visualisation_recipe, c(list(i), modelbased_args))
#   plot(out) + ggplot2::ggtitle("")
# })
all_plots <- lapply(text_modelbased, function(i) {
  out <- visualisation_recipe(i, show_data = "none")
  plot(out) + ggplot2::ggtitle("")
})

see::plots(all_plots, n_columns = round(sqrt(length(text_modelbased))))
```


```{r, eval=!is_supported}
cat(unsupported_message)
```

Column {data-width=400}
-----------------------------------------------------------------------

### Tabular summary

```{r, eval=is_supported, results="asis"}
for (i in text_modelbased) {
  tmp <- print_md(i)
  tmp <- gsub("Variable predicted", "\nVariable predicted", tmp)
  tmp <- gsub("Predictors modulated", "\nPredictors modulated", tmp)
  tmp <- gsub("Predictors controlled", "\nPredictors controlled", tmp)
  print(tmp)
}
```


```{r, eval=!is_supported}
cat(unsupported_message)
```


Text reports
=====================================    

Column {data-width=500}
-----------------------------------------------------------------------

### Textual summary

```{r, eval=is_supported, results='asis', collapse=TRUE}
# `{report}`
text_report <- report(model)
text_report_performance <- report_performance(model)

gsub("]", ")", gsub("[", "(", text_report, fixed = TRUE), fixed = TRUE)
cat("\n")
gsub("]", ")", gsub("[", "(", text_report_performance, fixed = TRUE), fixed = TRUE)
```


```{r, eval=!is_supported}
cat(unsupported_message)
```

Column {data-width=500}
-----------------------------------------------------------------------

### Model information

```{r, eval=is_supported}
model_info_data <- insight::model_info(model)

model_info_data <- datawizard::data_to_long(as.data.frame(model_info_data))

DT::datatable(model_info_data)
```

```{r, eval=!is_supported}
cat(unsupported_message)
```